1 Setup

Libraries

suppressPackageStartupMessages({
    library(data.table)
    library(DESeq2)
    library(gplots)
    library(here)
    library(hyperSpec)
    library(RColorBrewer)
    library(tidyverse)
    library(VennDiagram)
})

Helper files

suppressMessages({
    source(here("UPSCb-common/Rtoolbox/src/plotEnrichedTreemap.R"))
    source(here("UPSCb-common/src/R/featureSelection.R"))
    source(here("UPSCb-common/src/R/volcanoPlot.R"))
    source(here("UPSCb-common/src/R/gopher.R"))
})

Graphics

pal=brewer.pal(8,"Dark2")
hpal <- colorRampPalette(c("blue","white","red"))(100)
mar <- par("mar")

Functions

plot specific gene expression

"line_plot" <- function(dds=dds,vst=vst,gene_id=gene_id){
    message(paste("Plotting",gene_id))
    sel <- grepl(gene_id,rownames(vst))
    stopifnot(sum(sel)==1)

    p <- ggplot(bind_cols(as.data.frame(colData(dds)),
                          data.frame(value=vst[sel,])),
                aes(x=Level,y=value,col=Level,group=Level)) +
        geom_point() + geom_smooth() +
        scale_y_continuous(name="VST expression") + 
        ggtitle(label=paste("Expression for: ",gene_id))
    
    suppressMessages(suppressWarnings(plot(p)))
    return(NULL)
}

extract the DE results. Default cutoffs are from Schurch et al., RNA, 2016

"extract_results" <- function(dds,vst,contrast,
                              padj=0.01,lfc=0.5,
                              plot=TRUE,verbose=TRUE,
                              export=TRUE,default_dir=here("data/analysis/DE"),
                              default_prefix="DE-",
                              labels=colnames(dds),
                              sample_sel=1:ncol(dds),
                              expression_cutoff=0,
                              debug=FALSE,filter=c("median",NULL),...){
  
  # get the filter
  if(!is.null(match.arg(filter))){
    filter <- rowMedians(counts(dds,normalized=TRUE))
    message("Using the median normalized counts as default, set filter=NULL to revert to using the mean")
  }
  
  # validation
  if(length(contrast)==1){
    res <- results(dds,name=contrast,filter = filter)
  } else {
    res <- results(dds,contrast=contrast,filter = filter)
  }
  
  stopifnot(length(sample_sel)==ncol(vst))
  
  if(plot){
    par(mar=c(5,5,5,5))
    volcanoPlot(res)
    par(mar=mar)
  }
  
  # a look at independent filtering
  if(plot){
    plot(metadata(res)$filterNumRej,
         type="b", ylab="number of rejections",
         xlab="quantiles of filter")
    lines(metadata(res)$lo.fit, col="red")
    abline(v=metadata(res)$filterTheta)
  }
  
  if(verbose){
    message(sprintf("The independent filtering cutoff is %s, removing %s of the data",
                    round(metadata(res)$filterThreshold,digits=5),
                    names(metadata(res)$filterThreshold)))
    
    max.theta <- metadata(res)$filterNumRej[which.max(metadata(res)$filterNumRej$numRej),"theta"]
    message(sprintf("The independent filtering maximises for %s %% of the data, corresponding to a base mean expression of %s (library-size normalised read)",
                    round(max.theta*100,digits=5),
                    round(quantile(counts(dds,normalized=TRUE),probs=max.theta),digits=5)))
  }
  
  if(plot){
    qtl.exp=quantile(counts(dds,normalized=TRUE),probs=metadata(res)$filterNumRej$theta)
    dat <- data.frame(thetas=metadata(res)$filterNumRej$theta,
                      qtl.exp=qtl.exp,
                      number.degs=sapply(lapply(qtl.exp,function(qe){
                        res$padj <= padj & abs(res$log2FoldChange) >= lfc & 
                          ! is.na(res$padj) & res$baseMean >= qe
                      }),sum))
    if(debug){
      plot(ggplot(dat,aes(x=thetas,y=qtl.exp)) + 
             geom_line() + geom_point() +
             scale_x_continuous("quantiles of expression") + 
             scale_y_continuous("base mean expression") +
             geom_hline(yintercept=expression_cutoff,
                        linetype="dotted",col="red"))
      
      p <- ggplot(dat,aes(x=thetas,y=qtl.exp)) + 
        geom_line() + geom_point() +
        scale_x_continuous("quantiles of expression") + 
        scale_y_log10("base mean expression") + 
        geom_hline(yintercept=expression_cutoff,
                   linetype="dotted",col="red")
      suppressMessages(suppressWarnings(plot(p)))
      
      plot(ggplot(dat,aes(x=thetas,y=number.degs)) + 
             geom_line() + geom_point() +
             geom_hline(yintercept=dat$number.degs[1],linetype="dashed") +
             scale_x_continuous("quantiles of expression") + 
             scale_y_continuous("Number of DE genes"))
      
      plot(ggplot(dat,aes(x=thetas,y=number.degs[1] - number.degs),aes()) + 
             geom_line() + geom_point() +
             scale_x_continuous("quantiles of expression") + 
             scale_y_continuous("Cumulative number of DE genes"))
      
      plot(ggplot(data.frame(x=dat$thetas[-1],
                             y=diff(dat$number.degs[1] - dat$number.degs)),aes(x,y)) + 
             geom_line() + geom_point() +
             scale_x_continuous("quantiles of expression") + 
             scale_y_continuous("Number of DE genes per interval"))
      
      plot(ggplot(data.frame(x=dat$qtl.exp[-1],
                             y=diff(dat$number.degs[1] - dat$number.degs)),aes(x,y)) + 
             geom_line() + geom_point() +
             scale_x_continuous("base mean of expression") + 
             scale_y_continuous("Number of DE genes per interval"))
      
      p <- ggplot(data.frame(x=dat$qtl.exp[-1],
                             y=diff(dat$number.degs[1] - dat$number.degs)),aes(x,y)) + 
        geom_line() + geom_point() +
        scale_x_log10("base mean of expression") + 
        scale_y_continuous("Number of DE genes per interval") + 
        geom_vline(xintercept=expression_cutoff,
                   linetype="dotted",col="red")
      suppressMessages(suppressWarnings(plot(p)))
    }
  }
  
  sel <- res$padj <= padj & abs(res$log2FoldChange) >= lfc & ! is.na(res$padj) & 
    res$baseMean >= expression_cutoff
  
  if(verbose){
    message(sprintf(paste(
      ifelse(sum(sel)==1,
             "There is %s gene that is DE",
             "There are %s genes that are DE"),
      "with the following parameters: FDR <= %s, |log2FC| >= %s, base mean expression > %s"),
      sum(sel),padj,
      lfc,expression_cutoff))
  }
  
  # proceed only if there are DE genes
  if(sum(sel) > 0){
    val <- rowSums(vst[sel,sample_sel,drop=FALSE])==0
    if (sum(val) >0){
      warning(sprintf(paste(
        ifelse(sum(val)==1,
               "There is %s DE gene that has",
               "There are %s DE genes that have"),
        "no vst expression in the selected samples"),sum(val)))
      sel[sel][val] <- FALSE
    } 
    
    if(export){
      if(!dir.exists(default_dir)){
        dir.create(default_dir,showWarnings=FALSE,recursive=TRUE,mode="0771")
      }
      write.csv(res,file=file.path(default_dir,paste0(default_prefix,"-results.csv")))
      write.csv(res[sel,],file.path(default_dir,paste0(default_prefix,"-genes.csv")))
    }
    if(plot & sum(sel)>1){
      heatmap.2(t(scale(t(vst[sel,sample_sel]))),
                distfun = pearson.dist,
                hclustfun = function(X){hclust(X,method="ward.D2")},
                trace="none",col=hpal,labRow = FALSE,
                labCol=labels[sample_sel],...
      )
    }
  }
  return(list(all=rownames(res[sel,]),
              up=rownames(res[sel & res$log2FoldChange > 0,]),
              dn=rownames(res[sel & res$log2FoldChange < 0,])))
}

extract and plot the enrichment results

extractEnrichmentResults <- function(enrichment,task="go",
                                     diff.exp=c("all","up","dn"),
                                     go.namespace=c("BP","CC","MF"),
                                     genes=NULL,export=TRUE,plot=TRUE,
                                     default_dir=here("data/analysis/DE"),
                                     default_prefix="DE",
                                     url="athaliana"){
    # process args
    diff.exp <- match.arg(diff.exp)
    de <- ifelse(diff.exp=="all","none",
                 ifelse(diff.exp=="dn","down",diff.exp))

    # sanity
    if( is.null(enrichment[[task]]) | length(enrichment[[task]]) == 0){
        message(paste("No enrichment for",task))
    } else {

        # write out
        if(export){
            write_tsv(enrichment[[task]],
                      file=here(default_dir,
                                paste0(default_prefix,"-genes_GO-enrichment.tsv")))
            if(!is.null(genes)){
                write_tsv(
                    enrichedTermToGenes(genes=genes,terms=enrichment[[task]]$id,url=url,mc.cores=16L),
                    file=here(default_dir,
                              paste0(default_prefix,"-enriched-term-to-genes.tsv"))
                )
            }
        }
        
        if(plot){
            sapply(go.namespace,function(ns){
                titles <- c(BP="Biological Process",
                            CC="Cellular Component",
                            MF="Molecular Function")
                suppressWarnings(tryCatch({plotEnrichedTreemap(enrichment,enrichment=task,
                                                               namespace=ns,
                                                               de=de,title=titles[ns])},
                                          error = function(e) {
                                              message(paste("Treemap plot failed for",ns, 
                                                            "because of:",e))
                                              return(NULL)
                                          }))
            })
        }
    }
}

Data

load(here("data/analysis/salmon/dds_merge_expr_genes.rda"))

1.1 Normalisation for visualisation

vsd <- varianceStabilizingTransformation(dds,blind=FALSE)

##   Note: levels of factors in the design contain characters other than
##   letters, numbers, '_' and '.'. It is recommended (but not required) to use
##   only letters, numbers, and delimiters '_' or '.', as these are safe characters
##   for column names in R. [This is a message, not a warning or an error]

## using 'avgTxLength' from assays(dds), correcting for library size

vst <- assay(vsd)
vst <- vst - min(vst)
dir.create(here("data/analysis/DE"),showWarnings=FALSE)
save(vst,file=here("data/analysis/DE/vst-aware-exprGenes.rda"))
write_delim(as.data.frame(vst) %>% rownames_to_column("ID"),
            here("data/analysis/DE/vst-aware-exprGenes.tsv"))

2 Gene of interests

3 Differential Expression

dds <- DESeq(dds)

## estimating size factors

##   Note: levels of factors in the design contain characters other than
##   letters, numbers, '_' and '.'. It is recommended (but not required) to use
##   only letters, numbers, and delimiters '_' or '.', as these are safe characters
##   for column names in R. [This is a message, not a warning or an error]

## using 'avgTxLength' from assays(dds), correcting for library size

## estimating dispersions

## gene-wise dispersion estimates

## mean-dispersion relationship

##   Note: levels of factors in the design contain characters other than
##   letters, numbers, '_' and '.'. It is recommended (but not required) to use
##   only letters, numbers, and delimiters '_' or '.', as these are safe characters
##   for column names in R. [This is a message, not a warning or an error]

## final dispersion estimates

##   Note: levels of factors in the design contain characters other than
##   letters, numbers, '_' and '.'. It is recommended (but not required) to use
##   only letters, numbers, and delimiters '_' or '.', as these are safe characters
##   for column names in R. [This is a message, not a warning or an error]

## fitting model and testing

Dispersion estimation

plotDispEsts(dds)

Check the different contrasts

resultsNames(dds)

## [1] "Intercept"              "Level_60._vs_80."       "Level_40._vs_80."      
## [4] "Level_30._vs_80."       "Level_30.7d_vs_80."     "Level_Collapse_vs_80." 
## [7] "Level_C2d_vs_80."       "Level_Rehydrate_vs_80."

4 Results

Evaluate the contrast C2d vs 80, the 80% water content in the soil is our control.

4.1 Contrast 60 vs 80

contrast_60_vs_80 <- extract_results(dds=dds,vst=vst,contrast="Level_60._vs_80.", sample_sel = dds$Level %in% c("80%","60%"), labels = dds$Level, default_prefix="DE-60vs80")

## Using the median normalized counts as default, set filter=NULL to revert to using the mean

## Loading required package: LSD

## The independent filtering cutoff is 3.92665, removing 16.96089% of the data

## The independent filtering maximises for 20.59061 % of the data, corresponding to a base mean expression of 5.67702 (library-size normalised read)

## There are 0 genes that are DE with the following parameters: FDR <= 0.01, |log2FC| >= 0.5, base mean expression > 0

4.2 Contrast 40 vs 80

contrast_40_vs_80 <- extract_results(dds=dds,vst=vst,contrast="Level_40._vs_80.", sample_sel = dds$Level %in% c("80%","40%"),labels = dds$Level, default_prefix="DE-40vs80")

## Using the median normalized counts as default, set filter=NULL to revert to using the mean

## The independent filtering cutoff is 1.82587, removing 11.5163% of the data

## The independent filtering maximises for 16.96089 % of the data, corresponding to a base mean expression of 3.42178 (library-size normalised read)

## There are 49 genes that are DE with the following parameters: FDR <= 0.01, |log2FC| >= 0.5, base mean expression > 0

4.3 Contrast 30 vs 80

contrast_30_vs_80 <- extract_results(dds=dds,vst=vst,contrast="Level_30._vs_80.", sample_sel = dds$Level %in% c("80%","30%"),labels = dds$Level, default_prefix="DE-30vs80")

## Using the median normalized counts as default, set filter=NULL to revert to using the mean

## The independent filtering cutoff is 1.82587, removing 11.5163% of the data

## The independent filtering maximises for 11.5163 % of the data, corresponding to a base mean expression of 1.09435 (library-size normalised read)

## There are 1354 genes that are DE with the following parameters: FDR <= 0.01, |log2FC| >= 0.5, base mean expression > 0

4.4 Contrast 307d vs 80

contrast_307d_vs_80 <- extract_results(dds=dds,vst=vst,contrast="Level_30.7d_vs_80.", sample_sel = dds$Level %in% c("80%","30%7d"),labels = dds$Level, default_prefix="DE-307dvs80")

## Using the median normalized counts as default, set filter=NULL to revert to using the mean

## The independent filtering cutoff is 1.13544, removing 9.701434% of the data

## The independent filtering maximises for 11.5163 % of the data, corresponding to a base mean expression of 1.09435 (library-size normalised read)

## There are 590 genes that are DE with the following parameters: FDR <= 0.01, |log2FC| >= 0.5, base mean expression > 0

4.5 Contrast collapse vs 80

contrast_Collapse_vs_80 <- extract_results(dds=dds,vst=vst,contrast="Level_Collapse_vs_80.", sample_sel = dds$Level %in% c("80%","Collapse"),labels = dds$Level, default_prefix="DE-collapsevs80")

## Using the median normalized counts as default, set filter=NULL to revert to using the mean

## The independent filtering cutoff is 0.20464, removing 6.071708% of the data

## The independent filtering maximises for 9.70143 % of the data, corresponding to a base mean expression of 0.89866 (library-size normalised read)

## There are 7375 genes that are DE with the following parameters: FDR <= 0.01, |log2FC| >= 0.5, base mean expression > 0

4.6 Contrast C2d vs control

4.6.1 Show the heatmap just for the contrast we are interested in (C2d vs control)

contrast_C2d_vs_80 <- extract_results(dds=dds,vst=vst,contrast="Level_C2d_vs_80.", sample_sel = dds$Level %in% c("80%","C2d"), labels = dds$Level, default_prefix="DE-C2dvs80")

## Using the median normalized counts as default, set filter=NULL to revert to using the mean

## The independent filtering cutoff is 0.20464, removing 6.071708% of the data

## The independent filtering maximises for 6.07171 % of the data, corresponding to a base mean expression of 0 (library-size normalised read)

## There are 10544 genes that are DE with the following parameters: FDR <= 0.01, |log2FC| >= 0.5, base mean expression > 0

4.6.2 Change the value of the log2fc to remove bias due to the different amount of gene expressed in the two conditions

contrast_C2d_vs_80_l2fc2 <- extract_results(dds=dds,vst=vst,contrast="Level_C2d_vs_80.", sample_sel = dds$Level %in% c("80%","C2d"), labels = dds$Level, default_prefix="DE-C2dvs80-lfc2", lfc=2)

## Using the median normalized counts as default, set filter=NULL to revert to using the mean

## The independent filtering cutoff is 0.20464, removing 6.071708% of the data

## The independent filtering maximises for 6.07171 % of the data, corresponding to a base mean expression of 0 (library-size normalised read)

## There are 5289 genes that are DE with the following parameters: FDR <= 0.01, |log2FC| >= 2, base mean expression > 0

4.7 Contrast rehydrate vs 80

contrast_Rehydrate_vs_80 <- extract_results(dds=dds,vst=vst,contrast="Level_Rehydrate_vs_80.", sample_sel = dds$Level %in% c("80%","Rehydrate"),labels = dds$Level, default_prefix="DE-Rehydratevs80")

## Using the median normalized counts as default, set filter=NULL to revert to using the mean

## The independent filtering cutoff is 0.20464, removing 6.071708% of the data

## The independent filtering maximises for 9.70143 % of the data, corresponding to a base mean expression of 0.89866 (library-size normalised read)

## There are 1790 genes that are DE with the following parameters: FDR <= 0.01, |log2FC| >= 0.5, base mean expression > 0

colors <- c("red", "blue")
contrasts_name <- c("60_vs_80", "40_vs_80", "30_vs_80", "307d_vs_80",
               "Collapse_vs_80", "C2d_vs_80", "C2d_vs_80_l2fc2", "Rehydrate_vs_80")

contrasts <- c(contrast_60_vs_80, contrast_40_vs_80, contrast_30_vs_80, contrast_307d_vs_80,
               contrast_Collapse_vs_80, contrast_C2d_vs_80, contrast_C2d_vs_80_l2fc2, contrast_Rehydrate_vs_80)

matrix_contrast <- matrix(lapply(contrasts[which(names(contrasts) %in% c("up", "dn"))], function(x){length(x)}), nrow = 8, ncol = 2, byrow = TRUE) 

dimnames(matrix_contrast) <- list(contrasts_name,c("up", "down"))

end_point = 0.5 + nrow(matrix_contrast) + nrow(matrix_contrast) - 1

barplot(t(matrix_contrast), main="Number of DEG in different contrasts", ylab="Number of genes", xlab="", xaxt="n", space=1,
             col=colors, las=2, cex.names = 0.6, cex.axis = 0.8)
legend("topleft", colnames(matrix_contrast), cex=0.8, fill=colors)
text(seq(1.5, end_point, by=2), par("usr")[3]-0.25, srt=60, adj=1, xpd=TRUE, labels=paste(rownames(matrix_contrast)), cex=0.7)

4.8 Venn Diagram

4.8.0.1 All DE genes

4.8.0.2 DE genes (up in mutant)

4.8.0.3 DE genes (up in control)

4.9 Gene Ontology enrichment

4.9.1 GO of all, up and down regulated genes of the last contrast (log2fc=2)

background <- rownames(vst)[featureSelect(vst,dds$Level,exp=0.4)]

res.list <- contrast_C2d_vs_80

enr.list <- lapply(res.list, gopher,background=background, alpha=0.05, task="go",url="picab02", endpoint = "enrichment")

4.9.1.1 Go of all DE genes

dev.null <- extractEnrichmentResults(enr.list$all, diff.exp= "all", url="piceab02")

## Loading required package: treemap

4.9.1.2 Go of upregulated DE genes

dev.null <- extractEnrichmentResults(enr.list$up, diff.exp= "up", url="piceab02")

4.9.1.3 Go of downregulated DE genes

dev.null <- extractEnrichmentResults(enr.list$dn, diff.exp= "dn", url="piceab02")

Note: up and down refers to C2d so the upregulated genes are upregulated in C2d and the same is true for the downregulated one

5 Session Info

Session Info

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.6 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] grid      stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] treemap_2.4-3               jsonlite_1.8.4             
##  [3] LSD_4.1-0                   limma_3.52.4               
##  [5] VennDiagram_1.7.3           futile.logger_1.4.3        
##  [7] forcats_1.0.0               stringr_1.5.0              
##  [9] dplyr_1.1.0                 purrr_1.0.1                
## [11] readr_2.1.3                 tidyr_1.3.0                
## [13] tibble_3.1.8                tidyverse_1.3.2            
## [15] RColorBrewer_1.1-3          hyperSpec_0.100.0          
## [17] xml2_1.3.3                  ggplot2_3.4.1              
## [19] lattice_0.20-45             here_1.0.1                 
## [21] gplots_3.1.3                DESeq2_1.36.0              
## [23] SummarizedExperiment_1.28.0 Biobase_2.58.0             
## [25] MatrixGenerics_1.10.0       matrixStats_0.63.0         
## [27] GenomicRanges_1.50.1        GenomeInfoDb_1.34.4        
## [29] IRanges_2.32.0              S4Vectors_0.36.1           
## [31] BiocGenerics_0.44.0         data.table_1.14.6          
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.4.1           backports_1.4.1        igraph_1.3.5          
##   [4] lazyeval_0.2.2         splines_4.2.0          BiocParallel_1.32.4   
##   [7] gridBase_0.4-7         digest_0.6.31          htmltools_0.5.4       
##  [10] fansi_1.0.4            magrittr_2.0.3         memoise_2.0.1         
##  [13] googlesheets4_1.0.1    tzdb_0.3.0             Biostrings_2.66.0     
##  [16] annotate_1.74.0        modelr_0.1.10          vroom_1.6.1           
##  [19] timechange_0.2.0       jpeg_0.1-10            colorspace_2.1-0      
##  [22] blob_1.2.3             rvest_1.0.3            haven_2.5.1           
##  [25] xfun_0.36              crayon_1.5.2           RCurl_1.98-1.10       
##  [28] genefilter_1.78.0      survival_3.5-0         glue_1.6.2            
##  [31] gtable_0.3.1           gargle_1.3.0           zlibbioc_1.44.0       
##  [34] XVector_0.38.0         DelayedArray_0.24.0    scales_1.2.1          
##  [37] futile.options_1.0.1   DBI_1.1.3              Rcpp_1.0.10           
##  [40] xtable_1.8-4           bit_4.0.5              httr_1.4.4            
##  [43] ellipsis_0.3.2         pkgconfig_2.0.3        XML_3.99-0.13         
##  [46] sass_0.4.5             dbplyr_2.3.0           deldir_1.0-6          
##  [49] locfit_1.5-9.7         utf8_1.2.3             tidyselect_1.2.0      
##  [52] rlang_1.0.6            later_1.3.0            AnnotationDbi_1.60.0  
##  [55] munsell_0.5.0          cellranger_1.1.0       tools_4.2.0           
##  [58] cachem_1.0.6           cli_3.6.0              generics_0.1.3        
##  [61] RSQLite_2.2.20         broom_1.0.3            evaluate_0.20         
##  [64] fastmap_1.1.0          yaml_2.3.7             knitr_1.42            
##  [67] bit64_4.0.5            fs_1.6.0               caTools_1.18.2        
##  [70] KEGGREST_1.38.0        mime_0.12              formatR_1.14          
##  [73] brio_1.1.3             compiler_4.2.0         rstudioapi_0.14       
##  [76] curl_5.0.0             png_0.1-8              testthat_3.1.6        
##  [79] reprex_2.0.2           geneplotter_1.74.0     bslib_0.4.2           
##  [82] stringi_1.7.12         highr_0.10             Matrix_1.5-3          
##  [85] vctrs_0.5.2            pillar_1.8.1           lifecycle_1.0.3       
##  [88] jquerylib_0.1.4        bitops_1.0-7           httpuv_1.6.8          
##  [91] R6_2.5.1               latticeExtra_0.6-30    promises_1.2.0.1      
##  [94] KernSmooth_2.23-20     codetools_0.2-18       lambda.r_1.2.4        
##  [97] gtools_3.9.4           assertthat_0.2.1       rprojroot_2.0.3       
## [100] withr_2.5.0            GenomeInfoDbData_1.2.9 parallel_4.2.0        
## [103] hms_1.1.2              rmarkdown_2.20         googledrive_2.0.0     
## [106] shiny_1.7.4            lubridate_1.9.1        interp_1.1-3

Differential Expression for expressed genes in drought roots of Norway spruce

Emanuela Damieto

2023-02-26

1 Setup

1.1 Normalisation for visualisation

2 Gene of interests

3 Differential Expression

4 Results

4.1 Contrast 60 vs 80

4.2 Contrast 40 vs 80

4.3 Contrast 30 vs 80

4.4 Contrast 307d vs 80

4.5 Contrast collapse vs 80

4.6 Contrast C2d vs control

4.6.1 Show the heatmap just for the contrast we are interested in (C2d vs control)

4.6.2 Change the value of the log2fc to remove bias due to the different amount of gene expressed in the two conditions

4.7 Contrast rehydrate vs 80

4.8 Venn Diagram

4.8.0.1 All DE genes

4.8.0.2 DE genes (up in mutant)

4.8.0.3 DE genes (up in control)

4.9 Gene Ontology enrichment

4.9.1 GO of all, up and down regulated genes of the last contrast (log2fc=2)

4.9.1.1 Go of all DE genes

4.9.1.2 Go of upregulated DE genes

4.9.1.3 Go of downregulated DE genes

5 Session Info